Triple
T3792928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Courtney |
E89699
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Courtney Cox |
E330421
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Courtney Cox | Statement: [Courtney, hasNotableBearer, Courtney Cox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Courtney Cox Context triple: [Courtney, hasNotableBearer, Courtney Cox]
-
A.
Courteney Cox
chosen
Courteney Cox is an American actress best known for playing Monica Geller on the hit television sitcom "Friends."
-
B.
Christina Applegate
Christina Applegate is an American actress and comedian known for her roles in the sitcom "Married... with Children" and numerous film and television comedies.
-
C.
Kristin Davis
Kristin Davis is an American actress best known for her role as Charlotte York on the television series "Sex and the City" and its related films.
-
D.
Anna Faris
Anna Faris is an American actress and comedian best known for her lead role in the Scary Movie film series and her work in both film and television comedy.
-
E.
Julie Bowen
Julie Bowen is an American actress best known for her Emmy-winning role as Claire Dunphy on the television sitcom "Modern Family."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69aed9597d6881909b6ee3b9de859223 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee769d68081908dcdd3d232dbb61c |
completed | March 9, 2026, 3:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f058f03881909aded87e7a74849f |
completed | March 14, 2026, 5:21 a.m. |
Created at: March 9, 2026, 3:15 p.m.